R: Constant for 'Matrix_eQTL_engine'.

modelLINEAR_CROSS {MatrixEQTL} | R Documentation

## Constant for `Matrix_eQTL_engine`.

### Description

Set parameter `useModel = modelLINEAR_CROSS` in the call of `Matrix_eQTL_main` to indicate that Matrix eQTL should include the interaction of SNP and the last covariate in the model and test for its significance.

### References

The package website: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

### See Also

See `Matrix_eQTL_engine` for reference and sample code.

### Examples

    library('MatrixEQTL')

    # Number of columns (samples)
    n = 25;

    # Number of covariates
    nc = 10;

    # Generate the standard deviation of the noise
    noise.std = 0.1 + rnorm(n)^2;

    # Generate the covariates
    cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);

    # Generate the vectors with single genotype and expression variables
    snps.mat = cvrt.mat %*% rnorm(nc) + rnorm(n);
    gene.mat = cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std +
                1 + 0.5 * snps.mat + snps.mat * cvrt.mat[,nc];

    # Create 3 SlicedData objects for the analysis
    snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
    gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
    cvrt1 = SlicedData$new( t(cvrt.mat) );

    # name of temporary output file
    filename = tempfile();

    # Call the main analysis function
    me = Matrix_eQTL_main(
        snps = snps1,
        gene = gene1,
        cvrt = cvrt1,
        output_file_name = filename,
        pvOutputThreshold = 1,
        useModel = modelLINEAR_CROSS,
        errorCovariance = diag(noise.std^2),
        verbose = TRUE,
        pvalue.hist = FALSE );
    # remove the output file
    unlink( filename );

    # Pull Matrix eQTL results - t-statistic and p-value
    beta = me$all$eqtls$beta;
    tstat = me$all$eqtls$statistic;
    pvalue = me$all$eqtls$pvalue;
    rez = c(beta = beta, tstat = tstat, pvalue = pvalue)
    # And compare to those from the linear regression in R
    {
        cat('\n\n Matrix eQTL: \n');
        print(rez);
        cat('\n R summary(lm()) output: \n')
        lmodel = lm( gene.mat ~ snps.mat + cvrt.mat + snps.mat*cvrt.mat[,nc],
                        weights = 1/noise.std^2);
        lmout = tail(summary(lmodel)$coefficients,1)[, c("Estimate", "t value", "Pr(>|t|)")];
        print( tail(lmout) );
    }

    # Results from Matrix eQTL and 'lm' must agree
    stopifnot(all.equal(lmout, rez, check.attributes = FALSE));

[Package MatrixEQTL version 2.2 Index]